A Data-Driven Method for Vehicle Speed Estimation
| Autor: | Stefano Feraco, Angelo Bonfitto |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Estimation
estimation Artificial neural network Computer science Mechanical Engineering Real-time computing Mean value road identification Transportation DSPACE Data-driven Identification (information) classification Pattern recognition (psychology) regression vehicle speed Electrical and Electronic Engineering vehicle speed artificial neural networks estimation regression classification road identification artificial neural networks Road condition |
| Zdroj: | Communications - Scientific letters of the University of Zilina. 23:B165-B177 |
| ISSN: | 2585-7878 1335-4205 |
| Popis: | This paper presents a method based on Artificial Neural Networks for estimation of the vehicle speed. The technique exploits the combination of two tasks: a) speed estimation by means of regression neural networks dedicated to different road conditions (dry, wet and icy); b) identification of the road condition with a pattern recognition neural network. The training of the networks is conducted with experimental datasets recorded during the driving sessions performed with a vehicle on different tracks. The effectiveness of the proposed approach is validated experimentally on the same car by deploying the algorithm on a dSPACE computing platform. The estimation accuracy is evaluated by comparing the obtained results to the measurement of an optical sensor installed on the vehicle and to the output of another estimation method, based on the mean value of velocity of the four wheels. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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